Method and system for measuring the velocity of a vessel relative to the bottom using velocity measuring correlation sonar

ABSTRACT

A method and a system for measuring the velocity of a vessel relative to the bottom using a velocity measuring correlation sonar are disclosed. The present invention provides a new theoretical expression for the bottom medium sonar array temporal and spatial correlation function. The velocity of the vessel relative to the bottom is derived by fitting experimental data to a theoretical function. The bottom medium sonar array temporal and spatial correlation function of the present invention is succinctly expressed in zero-rank Bessel function, and well coincided with experiments. The function is applicable not only to far field region, i.e. planar wave region, but also to Fraunhofer region, i.e. spherical wave region. Transmit transducers and receive transducers are multistatic in the velocity measuring correlation sonar of the present invention, so the transmit beam width and the receive beam width can be selected reasonably. The present invention is applicable to measuring the velocity of the vessel relative to the bottom with high measurement accuracy, small calculation load, good robustness and fast convergence.

TECHINICAL FIELD

The present invention is related to a sonar velocity measuring field,and more concretely to a method and system for measuring the velocity ofa vessel relative to the bottom using correlation velocity measuringsonar.

PRIOR ART

At present, methods for measuring velocity using correlation velocitymeasuring sonar are summarized as follows.

(1) U.S. Pat. No. 5,315,562, titled “Correlation Sonar System” inventedby S. E. Bradley et al. discloses correlation sonar used for measuringcurrent profile and velocities of a vessel in water relative to thebottom. This invention includes the following four aspects:

(A) A complex signal is transmitted. The complex signal'sautocorrelation function has two different peaks at delay τ=0 andτ=τ_(c). The previous technology of transmitting two pulses that maycause interferences between medium layers of the fluid is eliminated.

(B) A theoretical expression for sonar array temporal and spatialcorrelation function for fluid medium and bottom medium is introduced inseries forms, wherein bessel function and Legendary function areincluded, and a simplified expression based on experiences is proposedand adopted for signal processing because of its simplicity.

(C) Based on the maximum likelihood principle, by using the simplexmethod, the current velocities and the vessel's velocity relative to thebottom are derived by optimally fitting the theoretical and experimentalsonar array temporal and spatial correlation functions.

(D) A matched filter approach is used for detecting the seabed echoes.

(E) Some transmit transducers and receive transducers of the sonar arrayare homeostatic.

(2) U.S. Pat. No. 5,422,860, titled “Correlation Sonar System” inventedby S. E. Bradley et al. discloses a method to generate correlation sonarsignals. Pseudo random phase-coded signal, whose autocorrelationfunction has two different peaks at delay τ=0 and τ=τ_(c), istransmitted.

The methods for measuring vessel's velocities relative to the bottom hasobvious shortcomings: (1) The theoretical expression for sonar arraytemporal and spatial correlation function is so complex that it isdifficult to use in practice; but the simplified expression derived fromexperience does not have sufficient physical foundation. This is themost important technology of correlation velocity measuring sonarsystem. (2) It is not the best method to fit the theoretical andexperimental temporal and spatial correlation function by using simplexmethod based on the maximum likelihood principle. (3) It is also not thebest method to use a velocity corresponding to the maximum value of thesonar array temporal and spatial correlation function as an initialvalue of velocity estimation. (4) The tactic that some transmittransducers and receive transducers of the sonar array are homeostaticlimits the selection of transmit beam width and receive beamwidth.

SUMMARY OF THE INVENTION

The main objective of the invention is to provide a preferredtheoretical bottom medium sonar array temporal and spatial correlationfunction for fitting with experimental data. Another objective of theinvention is to improve the data processing method for data temporal andspatial correlation function. The last objective of the invention is toimprove the transducer geometry in the conventional sonar array.

In order to achieve the objectives mentioned above, the presentinvention provides a method for measuring velocities of the vesselrelative to the bottom using correlation velocity measuring sonar, themethod comprising steps of

(1) Select transmit code for acoustic pulses, whose autocorrelation hasa peak at a non-zero time delay;

(2) According to the transmit code, transmit acoustic pulses into fluidmedium, and receive echo signals backscattered by flow layers and thebottom medium;

(3) performer the step (4) if the echo signals in the step (2) includesbottom echo, otherwise return back to the step (1) if the echo signalsin the step (2) does not include the bottom echo;

(4) Demodulate and filter the echo signals of bottom medium, andcalculate a data bottom medium temporal and spatial correlation functionmatrix according to the bottom echo;

(5) extract a data matrix for fitting from the data bottom mediumtemporal and spatial correlation function matrix derived from the step(4); wherein the data matrix for fitting is said data bottom mediumtemporal and spatial correlation function matrix, or the data matrix forfitting is a localized data bottom medium temporal and spatialcorrelation function matrix, and the localized data bottom mediumtemporal and spatial correlation function matrix is derived from stepsof

(a) perform an absolute value operation on the data bottom mediumtemporal and spatial correlation function matrix to attain a data bottommedium temporal and spatial correlation function absolute value matrix,and elements of said data bottom medium temporal and spatial correlationfunction absolute value matrix having a maximum value E_(Max);

(b) set a threshold value χ, wherein 0<χ≦1, preferably 0.7<χ≦1, whereinthose elements in the absolute value matrix with numerical value lessthan χE_(Max) is set to zero, those elements with numerical value equalto or larger than χE_(Max) is retained, and the localized bottom mediumtemporal and spatial correlation function absolute matrix can be derivedby operating all the elements;

(6) set a search range of the unknown parameter ensemble

₁{{overscore (V)}_(1x), {overscore (V)}_(1y), γ}, wherein {overscore(V)}_(1x), {overscore (V)}_(1y) are average values of vessel'svelocities relative to the bottom in x, y directions respectively, γ iswidth factor;

(7) fit the data matrix for fitting derived from the step (5) with atheoretical function in the search range of the unknown parameterensemble

₁; the fitting algorithm uses a sequential quadratic programming methodbased on the maximum likelihood principle or based on the nonlinearleast square principle, the theoretical function beingφ(d, ₁, τ)=BJ ₀(γβ₂θ)

-   -   wherein B is a constant, θ is the incident angle of the acoustic        wave, τ is time delay, d is the distance between receive        elements of the sonar array, J₀(•) is zero-rank Bessel function;        ${\beta_{2} = {\frac{\omega_{0}}{c}( {( {{\tau\quad{\overset{\_}{V}}_{1x}} + d_{x}} )^{2} + ( {{\tau\quad{\overset{\_}{V}}_{1y}} + d_{y}} )^{2}} )^{1/2}}},$        wherein ω₀ is the central frequency of the transmit signal, c is        the velocity of sound, d_(x) and d_(y) are components of d in x        direction and y direction respectively;

(8) attain average values {{overscore (V)}_(1x), {overscore (V)}_(1y),}of velocities relative to the bottom obtained from the fitting results,and store the results.

The steps (1)˜(8) can be repeated for the next measurement of vessel'svelocity relative to the bottom. When repeating the step (6), a previousmeasured velocity of the vessel relative to the bottom or an averagevalue of multiple previous measured velocities of the vessel relative tothe bottom is used as the initial value of the search range of theunknown parameter ensemble

₁.

The present invention further provides a correlation velocity measuringsonar system, including a sonar array (200) and an electronic subsystem,the sonar array (200) having a transmit sonar array and a receive sonararray, and the electronic subsystem includes a computer (406), whereinthe computer (406) comprises:

An initialization module for initializing software and hardware;

A signal coding module for selecting transmits code for acoustic pulse,whose autocorrelation has a peak value at a non-zero time delay;

A transmit/receive module for transmitting acoustic pulses into fluidmedium, and receive echo signals backscattered by flow layers and bottommedium;

An identification module for identifying whether bottom echo is in theecho signals received by the transmit/receive module;

A bottom extraction module for extracting bottom echo from the echosignals received by the transmit/receive module;

A demodulation and filter module for demodulating and filtering thebottom echo in the bottom extraction module;

A matrix calculation module for calculating data bottom medium temporaland spatial correlation function matrix according to the demodulated andfiltered bottom echo;

a matrix extraction module for extracting a data matrix for fitting fromthe data bottom medium temporal and spatial correlation function matrixderived by the matrix calculation module; wherein the data matrix forfitting extracted by the matrix extraction module is the data bottommedium temporal and spatial correlation function absolute value matrix,or a localized bottom medium data temporal and spatial correlationfunction absolute value matrix, when the localized bottom medium datatemporal and spatial correlation function absolute value matrix is usedas the data matrix for fitting, the matrix extraction module comprises

an absolute value calculation unit for performing an absolute valueoperation on the data bottom medium temporal and spatial correlationfunction matrix to attain a data bottom medium temporal and spatialcorrelation function absolute value matrix; and

a localization unit for selecting a maximum value E_(Max) in the databottom medium temporal and spatial correlation function absolute valuematrix, and setting a threshold value χ, wherein 0<χ≦1, and for settingthose elements in the absolute value matrix with numerical value lessthan χE_(Max) to zero and retaining those elements with numerical valueequal to or larger than χE_(Max) to attain the localized bottom mediumtemporal and spatial correlation function absolute matrix by operatingall the elements;

a parameter module for storing the search range of the unknown parameterensemble

₁={{overscore (V)}_(1x), {overscore (V)}_(1y), γ}, wherein {overscore(V)}_(1x), {overscore (V)}_(1y) are average values of vessel'svelocities relative to the bottom in x, y directions respectively, γ iswidth factor, wherein the initial value of the search range of theunknown parameter ensemble

₁ stored in the parameter module is a previous measured velocity of thevessel relative to the bottom or an average value of multiple previousmeasured velocities of the vessel relative to the bottom;

A fit module for fitting the data matrix derived from the matrixextraction module with a theoretical function in the search range of theunknown parameter ensemble

₁; the fitting algorithm using a sequential quadratic programming methodbased on the maximum likelihood principle or based on the nonlinearleast square principle, the theoretical function beingφ(d, ₁, τ)=BJ ₀(γβ₂θ);

wherein B is a constant, θ is the incident angle of the acoustic wave, τis time delay, d is distance between receive elements of the sonararray, J₀(•) is zero-rank Bessel function; β₂=ω₀/c

τ{overscore (V)}_(1x)+d_(x))²+(τ{overscore (V)}_(1y)+d_(y))² ^(1/2),

wherein ω₀ is the central frequency of the transmit signal, c is thevelocity of sound, d_(x) and d_(y) are components of d in x directionand y direction respectively; and

A velocity storage module for storing average values {{overscore(V)}_(x), {overscore (V)}_(y),} of the vessel's velocities relative tothe bottom derived from fitting results of the fit module.

In the correlation velocity measuring sonar system of the presentinvention, the transmit sonar array composed of transmit transducers andthe receive sonar array composed of receive transducers are multistatic.The receive transducers in the receive sonar array are arranged toenable a maximum number of distance vectors among the receivetransducers. The transmit transducers in the transmit sonar arraytightly abut with each other. In an embodiment, the transmit sonar arrayincludes seven transmit transducers, and the receive sonar arrayincludes eight receive transducers.

The present invention has the following advantages:

(1) When measuring velocities of the vessel relative to the bottom, thetheoretical bottom medium sonar array temporal and spatial correlationfunction provided by the present invention is applicable not only to farfield region, i.e. planar wave region, but also Fraunhofer region, i.e.spherical wave region. However, the conventional acoustic correlationvelocity measuring theory is only applicable to the far field region, sothat it is difficult to attain good data in a relative largeshort-distance scope. The theory of the invention makes the scope less.Moreover, the bottom medium sonar array temporal and spatial correlationfunction of the invention is succinctly expressed by zero-rank Besselfunction and in good coincidence with experiments. The conventionaltheory is expressed in series forms of Bessel function and legendaryfunction, which is inconvenient in use, or is expressed in experientialformulas with no sufficient physical foundation.

(2) The fitting algorithm of the invention uses a sequential quadraticprogramming method based on the maximum likelihood principle, or basedon the nonlinear least square principle to fit measured data with thetheoretical sonar array temporal and spatial correlation function toattain velocities. Compared with the conventional simplex method, themethod of the present invention has faster convergence rate, highermeasurement accuracy. Especially, velocity estimation based on nonlinearleast square principle, compared with the maximum likelihood principle,has better robustness and small calculation load. In particular to thecorrelation velocity measuring sonar in actual situation, environmentalnoises may be uneven in space, the amplitudes and phrases of the receiveelements of the sonar array may disaccord from each other. These twofactors will affect the least square principle less than the maximumlikelihood principle.

(3) The present invention uses the method to calculate absolute value ofand to localize the data bottom medium temporal and spatial correlationfunction matrix and uses regions with large amplitudes in the matrix tocalculate velocities. The absolute value of the correlation function isonly related with the average horizontal velocities {overscore (V)}_(X)and {overscore (V)}_(Y) of the vessel, and regions with low signal noiseratio are eliminated. These two signal processing measures raise themeasurement accuracy.

(4) The invention uses the average value of measured velocities from theN-m^(th) time to the N^(th) time as the initial value of estimatedvelocity at the N+1^(th) time, which raises calculation speed andreduces hardware cost.

(5) The present invention provides a new sonar array with transducergeometry, wherein transmit transducers and receive transducers aremultistatic, so the transmit beam width and the receive beam width canbe selected reasonably.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view of a correlation velocity-measuring sonarsystem in operation;

FIG. 2 is a schematic view of the structure of the correlationvelocity-measuring sonar system;

FIG. 3 is a schematic view of the sonar array geometry of thecorrelation velocity measuring sonar system;

FIG. 4 is a flow chart of the software for the correlation velocitymeasuring sonar system;

FIG. 5 is a detailed flow chart of the step 620 in FIG. 4; and

FIG. 6 is a diagram of measured vessel velocity comparison between thecorrelation velocity measuring sonar system (ACL) and differential GPSat a water area 3500 m deep; wherein FIG. 6 a illustrates measuredvelocity amplitudes 701 and 703 by these two equipments, and FIG. 6 billustrates measured velocity directions 702 and 704 by these twoequipments.

Numerals:

Vessel 100 sonar array 200 underwater electronic subsystem 300

Dry end 400 terminal 500

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The present invention will be described in detail hereinafter inconjunction with the drawings and embodiments.

With reference to FIG. 1, a correlation velocity measuring sonar systemin accordance with the present invention, used for measuring velocitiesof vessel relative to the bottom, is installed on a vessel (100). Thecorrelation velocity measuring sonar system generally includes a sonararray (200) and an electronic subsystem. The electronic subsystemincludes an underwater electronic subsystem (300), a dry end (400) and aterminal (500). The sonar array (200) and underwater electronicsubsystem (300) are installed beneath the water, and the dry end (400)and terminal (500) are installed above the water. A transmit transducerarray of the sonar array (200) transmits acoustic pulses into the water.The acoustic pulse 102 in one pulse width spreads in the water andencounters the seabed in a ring (103) so as to generate a seabed echo.The seabed echo are received by the receive transducer array of thesonar array (200), and processed by the electronic subsystem tocalculate the velocity of the vessel.

The detailed structure of the correlation velocity measuring sonarsystem is illustrated in FIG. 2. The sonar array (200) includes receivetransducers (201) and transmit transducers (202). The receivetransducers (201) constitute the receive transducer array. The transmittransducers (203) constitute the transmit transducer array. A sonararray geometry (200) is illustrated in FIG. 3, wherein elements (1-7)are transmit transducers which tightly abut with each other; andelements (8-15) are receive transducers which are arranged under theprinciple of enabling a maximum number of distance vectors among thereceive transducers.

With reference to FIG. 2, the underwater electronic subsystem (300)includes multi-channel preamplifiers (301) connected to the receivetransducers (201). The underwater electronic subsystem (300) alsoincludes a temperature sensor (302), a water-leaking-detection sensor(303) and an attitude sensor (304), all connected to a sonar interfacecontrol board (407) in the dry end (400).

The dry end (400) includes a transmitter (401) connected to the transmittransducer (202), multi-channel receivers (402) connected to thepreamplifiers (302), a multi-channel synchronous AD converter board(403) connected to the multi-channel receivers (402), and a DSP board(404) connected to the multi-channel synchronous AD converter board(403). The dry end (400) also includes a computer (406) connected to theDSP board (404) and multi-channel synchronous AD converter board (403)respectively by a data/control bus (405). The dry end (400) alsoincludes the sonar interface control board (407) connected to themulti-channel receivers (402), the transmitter (401), the DSP board(404) and the computer (406) respectively, and an AC/DC power supply(408) connected to the sonar interface control board (407), themulti-channel receivers (402), the transmitter (401), the data/controlbus (405), the temperature sensor (303), the water-leaking-detectionsensor (304) and the attitude sensor (304) respectively. The dry end(400) also includes a GPS receiver (409) and a GYRO (410) connected tothe computer (406).

The terminal (500) includes a terminal computer (502) connected to thecomputer (406) by a network (501).

A special velocity measuring program is stored in the computer (406).The program includes initialization module, signal coding module,transmit/receive module, identification module, bottom extractionmodule, and demodulation and filter module, matrix calculation module,matrix extraction module, parameter module, fit module and velocitystorage module. The program is executed according to steps illustratedin FIG. 4.

The step (601) is the start, in which the terminal computer (502) sendsinstructions to the computer (406) by the network (501), and then theprogram in the computer (406) starts to enable the sonar system in anoperating state. In the steps (602) and (603), the initialization moduleinitializes software and system hardware. In the step (613), accordingto the depth of the bottom, signal coding module selects transmit code,whose autocorrelation has a peak value at a non-zero time delay. In thestep (614), transmit/receive module sends the instructions of thecomputer (406) through the data/control bus (405) to the DSP board(404), and the DSP board (404) send transmit signals to the transmitter(401) and the transducer (202) to send acoustic pulses into the fluidmedium. In the step (615), transmit/receive module control the receivetransducers (203) to receive echoes backscattered by the fluid mediumand seabed medium, and to feed the echoes to the multi-channel receivers(402) through the preamplifiers (302) and then to the DSP board (404)through the multi-channel synchronous AD converter board (403). In thestep (616), the identification module controls the DSP board (404) toidentify whether bottom echo is included in the received echoes. If theresult is no, the program returns back to the step (613); if the resultis yes, the program performs the step (617). In the step (617), thebottom extraction module controls the DSP board (404) to extract bottomecho from the echo signals. In the step (618), the demodulation modulecontrols the DSP board (404) to demodulate and filter the bottom echo.

In the step (619), matrix calculation module calculates the data bottommedium temporal and spatial correlation function matrix according to thedemodulated and filtered bottom echo signals.

In the step (620), the matrix extraction module extracts a data matrixfor fitting from the data bottom medium temporal and spatial correlationfunction matrix. This data matrix will be fitted with a theoreticalfunction provided by the present invention in the step (622). In detail,during the step (620), the matrix extraction module can directly use thedata bottom medium temporal and spatial correlation function matrixderived from the step (609) as the data matrix for fitting, or use thefurther processed data bottom medium temporal and spatial correlationfunction matrix derived from the step (609) as the data matrix forfitting. In the latter, matrix extraction module includes an absolutevalue calculation unit and a localization unit, for which a detailedflow charts, is illustrated in FIG. 5. With reference to FIG. 5, theabsolute value calculation unit performs an absolute value operation onthe data bottom medium temporal and spatial correlation function toattain an absolute value matrix of data bottom medium temporal andspatial correlation function. Then, the localization unit performs alocalization operation on the absolute value matrix of the data temporaland spatial correlation function. Finally, the localized matrix is usedas the data matrix for fitting. The localization means selecting themaximum value E_(Max) from the data bottom medium temporal and spatialcorrelation function absolute value matrix, and setting a thresholdvalue χ, wherein 0<χ≦1. Then, those elements in the absolute valuematrix with numerical value less than χE_(Max) is set to zero, thoseelements with numerical value equal to or larger than χE_(Max) isretained. The localized data bottom medium temporal and spatialcorrelation function absolute matrix can be derived by performing theoperation on all the elements. The localizing operation only chooses theelements larger than or equal to χE_(Max), i.e. chooses the region withhigh signal noise ratio and eliminates the region with low signal noiseratio, thus further simplifying calculation and improving measurementaccuracy. In practice, the threshold value χ is preferred between 0.7and 1.

After the data matrix for fitting is obtained, the fitting operation ofthe data matrix and theoretical function matrix is performed to attainthe vessel's velocity relative to the bottom from the fitting results.In accordance with the present invention, a theoretical bottom mediumsonar array temporal and spatial correlation function is expressed asfollowRs(τ,

, d)=B└exp{jf({overscore (V)} _(z))}┘J ₀(γβ₂θ)  (1)

wherein B is a function of f({overscore (V)}_(z)), f is a certainfunction, {overscore (V)}_(z) is an average of vessel's velocityrelative to the bottom in z direction, d is distance between receiveelements of the sonar array, τ is time delay, θ is the incident angle ofthe acoustic wave, J₀(•) is zero-rank Bessel function;${\beta_{2} = {\frac{\omega_{0\quad}}{c}( {( {{\tau\quad{\overset{\_}{V}}_{1x}} + d_{x}} )^{2} + ( {{\tau\quad{\overset{\_}{V}}_{1y}} + d_{y}} )^{2}} )^{1/2}}},$wherein ω₀ is the central frequency of the transmit signal, c is thevelocity of sound, d_(x) and d_(y) are components of d in x directionand y direction respectively.

According to the equation (1), Rs(τ,

, d) is related with {overscore (V)}_(x), {overscore (V)}_(y),{overscore (V)}_(z). If the three-dimension velocities are all estimatedtogether, the calculation is complex and the accuracy is low. Afterperforming absolute value operation on the theoretical bottom mediumsonar array temporal and spatial correlation function expressed inequation (1), an equation is expressed as follow:φ(d, ₁, τ)=|Rs(d, ₁, τ)|=BJ ₀(γβ₂θ)  (2)Where, B is a constant. A matrix constructed by absolution values of thetheoretical bottom medium temporal and spatial correlation functionexpressed in the equation (2), is called theoretical bottom mediumtemporal and spatial correlation function absolute value matrix, whichis related only with {overscore (V)}_(x), and {overscore (V)}y. Thiscalculation is succinct and the accuracy is high. In practice,{overscore (V)}_(x). {overscore (V)}_(y) are often sufficient. Moreover,{overscore (V)}_(z) can be measured by other devices.

In the step (621), the parameter module sets and stores a search rangeof the unknown ensemble

₁={{overscore (V)}_(x), {overscore (V)}_(y), γ}, wherein the searchrange of the unknown ensemble

₁ is set as large as possible at first measurement to include the truevelocity of the vessel relative to the bottom in the search range. Inthe following measurements, the previous measurement result or anaverage value of multiple previous measurement results is preferablyused as the initial value for the search range. Therefore, thecalculation speed is high, and the hardware cost is low.

In the step (622), the fit module controls the DSP board (404) to fitthe data matrix derived from the matrix extraction module during thestep (612) with the equation (2) so as to attain the average of vessel'svelocities relative to the bottom. Here, the fitting algorithm can be asequential quadratic programming method based on the maximum likelihoodprinciple, or preferably a sequential quadratic programming method basedon the nonlinear least square principle.

In the step (623), the velocity storage module feeds the fitting resultsderived from the step (623) to the computer (406) through thedata/control bus (405) and the computer stores the fitting results inthe memory. After the step (623), the program can return back to thestep (613) for the next measurement.

Finally, data from the temperature sensor (302), thewater-leaking-detection sensor (303) and the attitude sensor (304) arefed to the computer (406) by the sonar interface control board (407).The computer (406) cooperates data from the GPS (409) and GYRO (410) andthen sends the final results to the terminal computer (502) by thenetwork (501).

FIG. 6 illustrates diagrams of the vessel's velocities (100) relative tothe bottom measured by the correlation velocity measuring sonar systemwith 23.5 kHz central frequency, 4.4 kHz bandwidth of the presentinvention and a differential GPS respectively. FIG. 6 a illustrates theamplitudes of the vessel's velocities (701, 703) relative to the bottommeasured by these two apparatuses in a time interval, wherein theabscissa represents time, and the ordinate represents amplitude ofvelocity. FIG. 6 b illustrates directions (702, 704) of vessel'svelocities relative to the bottom measured by these two equipments in atime interval, wherein the abscissa represents time, and the ordinaterepresents direction. The curves (701, 703) represent data measured bythe correlation velocity measuring sonar system of the invention; andthe curves (702, 704) represent data measured by the differential GPS.The results by these two equipments are quite coincided with each other.

1. A method for measuring the vessel's velocity relative to the bottomusing correlation velocity measuring sonar, the method comprising stepsof (1) Select transmit code for acoustic pulses; (2) according to thetransmit code, transmit acoustic pulses into fluid medium, and receivingecho signals backscattered by flow layers and the bottom; (3) excute thestep (4) if the echo signals in the step (2) includes bottom echo,otherwise return back to the step (1) if the echo signals in the step(2) does not include bottom echo; (4) Demodulate and filter the bottomecho signals, and calculate the data bottom medium temporal and spatialcorrelation function matrix according to the bottom echo signals; (5)Extract the data matrix for fitting from the data bottom medium temporaland spatial correlation function matrix derived from the step (4); (6)set a search range of the unknown parameter ensemble

₁={{overscore (V)}_(1x), {overscore (V)}_(1y), γ}, wherein {overscore(V)}_(1x), {overscore (V)}_(1y) are average values of vessel'svelocities relative to the bottom in x, y directions respectively, γ iswidth factor; (7) Fit the data matrix for fitting derived from the step(5) with a theoretical function in the search range of the unknownparameter ensemble

₁; the theoretical function beingφ(d, ₁, τ)=BJ ₀(γβ₂θ) wherein B is a constant, θ is the incident angleof the acoustic wave, τ is time delay, d is distance between receiveelements of the sonar array, J₀(•) is zero-rank Bessel function;${\beta_{2} = {\frac{\omega_{0}}{c}( {( {{\tau\quad{\overset{\_}{V}}_{1x}} + d_{x}} )^{2} + ( {{\tau\quad{\overset{\_}{V}}_{1y}} + d_{y}} )^{2}} )^{1/2}}},$wherein ω₀ is the central frequency of the transmit signal, c is thevelocity of sound, d_(x) and d_(y) are components of d in x directionand y direction respectively; (8) Attain average values {{overscore(V)}_(1x), {overscore (V)}1y,} of velocities relative to the bottomderived from the fitting results, and storing the results.
 2. The methodfor measuring the vessel's velocity relative to the bottom usingcorrelation velocity measuring sonar as claimed in claim 1,characterized in that the steps (1)˜(8) are repeated for the nextmeasurement of the vessel's velocity relative to the bottom.
 3. Themethod for measuring the vessel's velocity relative to the bottom usingcorrelation velocity measuring sonar as claimed in claim 1,characterized in that the autocorrelation of the transmit code in thestep (1) has a peak value at a non-zero time delay.
 4. The method formeasuring the vessel's velocity relative to the bottom using correlationvelocity measuring sonar as claimed in claim 1, characterized in thatthe data matrix for fitting in the step (5) is said data bottom mediumtemporal and spatial correlation function matrix.
 5. The method formeasuring the vessel's velocity relative to the bottom using correlationvelocity measuring sonar as claimed in claim 1, characterized in thatthe data matrix for fitting in the step (5) is a localized data bottommedium temporal and spatial correlation function matrix, and thelocalized data bottom medium temporal and spatial correlation functionmatrix is derived from steps of (a) perform an absolute value operationon the data bottom medium temporal and spatial correlation functionmatrix to attain a data bottom medium temporal and spatial correlationfunction absolute value matrix, and elements of said data bottom mediumtemporal and spatial correlation function absolute value matrix having amaximum value E_(Max); (b) set a threshold value χ, wherein 0<χ≦1,wherein those elements in the absolute value matrix with numerical valueless than χE_(Max) is set to zero, those elements with numerical valueequal to or larger than χE_(Max) is retained, and the localized bottommedium temporal and spatial correlation function absolute matrix can bederived by operating all the elements.
 6. The method for measuring thevessel's velocity relative to the bottom using correlation velocitymeasuring sonar as claimed in claim 5, characterized in that thethreshold value in the step (b) is 0.7<χ≦1.
 7. The method for measuringthe vessel's velocity relative to the bottom using correlation velocitymeasuring sonar as claimed in claim 1, characterized in that in the step(7), the fitting algorithm uses a sequential quadratic programmingmethod based on the maximum likelihood principle.
 8. The method formeasuring the vessel's velocity relative to the bottom using correlationvelocity measuring sonar as claimed in claim 1, characterized in that inthe step (7), the fitting algorithm uses a sequential quadraticprogramming method based on the nonlinear least square principle.
 9. Themethod for measuring the vessel's velocity relative to the bottom usingcorrelation velocity measuring sonar as claimed in claim 2,characterized in that a previous measured velocity of the vesselrelative to the bottom or an average value of multiple previous measuredvelocities of the vessel relative to the bottom is used as the initialvalue of the search range of the unknown parameter ensemble

₁.
 10. A correlation velocity measuring sonar system for practicing themethod as claimed in claim 1, including a sonar array (200) and anelectronic subsystem, the sonar array (200) having a transmit array anda receive array and the electronic subsystem having a computer (406),characterized in that the computer (406) comprises: an initializationmodule for initializing software and hardware; A signal coding modulefor selecting transmits code for acoustic pulse; a transmit/receivemodule for transmitting acoustic pulses into fluid medium, and receivingecho signals backscattered by flow layers and bottom medium; Anidentification module for identifying whether bottom echo signals areincluded in the echo signals received by the transmit/receive module; Abottom extraction module for extracting bottom echo signals from theecho signals received by the transmit/receive module; A demodulation andfilter module for demodulating and filtering the echo signals of bottommedium in the bottom extraction module; A matrix calculation module forcalculating data bottom medium temporal and spatial correlation functionmatrix according to demodulated and filtered echo signals of bottommedium; A matrix extraction module for extracting a data matrix forfitting from the data bottom medium temporal and spatial correlationfunction matrix derived from the matrix calculation module; a parametermodule for storing the search range of the unknown parameter ensemble

₁={{overscore (V)}_(1x), {overscore (V)}_(1y), γ}, wherein {overscore(V)}_(1x), {overscore (V)}_(1y) are average values of vessel'svelocities relative to the bottom in x, y directions respectively, γ iswidth factor; A fit module for fitting the data matrix derived from thematrix extraction module with a theoretical function in the search rangeof the unknown parameter ensemble

₁; the theory function beingφ(d, ₁, τ)=BJ ₀(γβ₂θ); wherein B is a constant, θ is the incident angleof the acoustic wave, τ is time delay, d is distance between receiveelements of the sonar array, J₀(•) is zero-rank Bessel function;${\beta_{2} = {\frac{\omega_{0}}{c}( {( {{\tau\quad{\overset{\_}{V}}_{1x}} + d_{x}} )^{2} + ( {{\tau\quad{\overset{\_}{V}}_{1y}} + d_{y}} )^{2}} )^{1/2}}},$wherein ω₀ is the central frequency of the transmit signal, c is thevelocity of sound, d_(x) and d_(y) are components of d in x directionand y direction respectively; and A velocity storage module for storingaverage values {{overscore (V)}_(x), {overscore (V)}_(y),} of thevessel's velocities relative to the bottom derived from fitting resultsof the fit module.
 11. The correlation velocity measuring sonar systemas claimed in claim 10, characterized in that the transmit codegenerated by the signal coding module has a correlation peak value at anon-zero time delay.
 12. The correlation velocity measuring sonar systemas claimed in claim 10, characterized in that the data matrix forfitting extracted by the matrix extraction module is the data bottommedium temporal and spatial correlation function matrix.
 13. Thecorrelation velocity measuring sonar system as claimed in claim 10,characterized in that the data matrix for fitting extracted by thematrix extraction module is a localized bottom medium data temporal andspatial correlation function absolute value matrix, and the matrixextraction module comprises an absolute value calculation unit forperforming an absolute value operation on the data bottom mediumtemporal and spatial correlation function matrix to attain a data bottommedium temporal and spatial correlation function absolute value matrix;and a localization unit for selecting a maximum value E_(Max) in thedata bottom medium temporal and spatial correlation function absolutevalue matrix, and setting a threshold value χ, wherein 0<χ≦1, and forsetting those elements in the absolute value matrix with numerical valueless than χE_(Max) to zero and retaining those elements with numericalvalue equal to or larger than χE_(Max) to attain the localized bottommedium temporal and spatial correlation function absolute matrix byoperating all the elements.
 14. The correlation velocity measuring sonarsystem as claimed in claim 10, characterized in that the fit module is acalculation module using a sequential quadratic programming method basedon the maximum likelihood principle for fitting operation.
 15. Thecorrelation velocity measuring sonar system as claimed in claim 10,characterized in that the fit module is a calculation module using asequential quadratic programming method based on the nonlinear leastsquare principle for fitting operation.
 16. The correlation velocitymeasuring sonar system as claimed in claim 10, characterized in that theinitial value of the search range of the unknown parameter ensemble

₁ stored in the parameter module is a previous measured velocity of thevessel relative to the bottom or an average value of multiple previousmeasured velocities of the vessel relative to the bottom.
 17. Thecorrelation velocity measuring sonar system as claimed in claim 10,characterized in that the transmit array being composed of transmittransducers and the receive array being composed of receive transducersare multistatic.
 18. The correlation velocity measuring sonar system asclaimed in claim 17, characterized in that the receive elements in thereceive array are arranged to enable a maximum number of distancevectors among the receive transducers.
 19. The correlation velocitymeasuring sonar system as claimed in claim 17, characterized in that thetransmit transducers in the transmit array tightly abut with each other.20. The correlation velocity measuring sonar system as claimed in claim19, characterized in that the transmit array includes seven transmittransducers, and the receive array includes eight receive transducers.